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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "seqlength = 20\n",
    "num_midi_features = 3\n",
    "num_sequences_per_song = 2\n",
    "training_rate = 0.8\n",
    "validation_rate = 0.1\n",
    "test_rate = 0.1\n",
    "\n",
    "syll_model_path = '../data/syllEncoding_20190419.bin'\n",
    "word_model_path = '../data/wordLevelEncoder_20190419.bin'\n",
    "songs_path = './data/songs_word_level'\n",
    "\n",
    "print('Creating a dataset with sequences of length', seqlength,\n",
    "      'with', num_sequences_per_song, 'sequences per song')\n",
    "\n",
    "syllModel = Word2Vec.load(syll_model_path)\n",
    "wordModel = Word2Vec.load(word_model_path)\n",
    "syll2Vec = syllModel.wv['Hello']\n",
    "word2Vec = wordModel.wv['world']\n",
    "num_syll_features = len(syll2Vec) + len(word2Vec)\n",
    "print('Syllable embedding length :', 3)"
   ],
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     "name": "#%%\n"
    }
   }
  }
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